Adventures in Computation

Sunday, February 22, 2015

Sanjeev Khanna and Chandra Chekuri are co-chairing the STOC 2015 workshops and tutorials, and are looking for submissions. The call is below:

Call for STOC 2015 Workshop and Tutorial Proposals

Workshop and Tutorial Day: Sunday, June 14, 2015

Workshop and Tutorial Co-Chairs: Chandra Chekuri and Sanjeev Khanna

Submission deadline: March 20, 2015

Notification: March 30, 2015

On Sunday, June 14, immediately preceding the main conference, STOC 2015 will hold a workshop-and-tutorials day. We invite groups of interested researchers to submit workshop or tutorial proposals. The goal of a workshop is to provide an informal forum for researchers to discuss important research questions, directions, and challenges. Connections between theoretical computer science and other areas, topics that are not well represented at STOC, and open problems are encouraged as workshop topics. Organizers are completely free to choose their workshop formats (invited speakers, panel discussions, etc.). The program for June 14th may also involve tutorials, each consisting of 1-2 survey talks on a particular area, and we welcome tutorial proposals as well.
STOC does not have funds to pay travel expenses or honoraria to invited workshop and tutorial speakers. Workshop and tutorials attendance will be free, and there is no separate registration for attending them. STOC will support coffee breaks for the workshops/tutorials attendees but no lunch will be provided. Note that STOC registration neither includes FCRC registration for Sunday, June 14 nor covers lunch for Sunday, June 14. Workshop/tutorial attendees who wish to attend another FCRC conference on June 14, would need to register for that conference.

Proposal submission

Workshop and tutorial proposals should be no longer than 2 pages. Please include a list of names and email addresses of the organizers, a description of the topic and the goals of the workshop or tutorial, the proposed workshop format (invited talks, contributed talks, panel, etc.), and proposed or tentatively confirmed speakers if known. Please also indicate the preferred length of time for your workshop or tutorial, along with the minimum acceptable time. We anticipate a 4-5 hour block for each workshop and a 2-4 hour block for each tutorial. Please feel free to contact the Workshop and Tutorial Co-Chairs at the email addresses below if you have questions about workshop or tutorial proposals.

Submission deadline

Proposals should be submitted by March 20, 2015, via email to chekuri@illinois.edu and sanjeev@cis.upenn.edu. Proposers will be notified by March 30, 2015, about whether their proposals have been accepted.

Wednesday, January 28, 2015

Patrick Loiseau, Adam Wierman, and I are co-chairing the 2015 NetEcon workshop, to be held in conjunction with EC and Sigmetrics at this year's FCRC in Portland. You should stop by if you are attending any of the FCRC conferences, (and submit a paper). If you haven't been to a previous iteration, this workshop brings together people interested in game theory from both the networking and theory/AI communities, so is a great place to present work that might be of interest to both communities, or to pick up problems that are interesting from a different community than yours. We've got a great set of invited speakers: R. Srikant, Ricky Vohra, and Eva Tardos. So start thinking about what you want to submit -- you've only got about 3 months.

Tuesday, November 25, 2014

There will be a differential privacy workshop in London in April, accepting submissions soon. If you have something you are working on, consider submitting here. Some highlights:

-- Although the workshop will not have proceedings (so you can as usual submit a talk and still submit it to a conference of your choosing), exceptional submissions will be invited to a special issue of the Journal of Privacy and Confidentiality.

-- The PC is pretty inter-disciplinary. This is certainly not just a Theory-A workshop. Work on privacy in all areas is on topic.

-- You get to hear Jon Ullman give a (presumably excellent) talk.

CALL FOR PAPERS
TPDP 2015
First workshop on the Theory and Practice of Differential Privacy
18th April 2015, London, UK
Affiliated to ETAPS
http://tpdp.computing.dundee.ac.uk
Differential privacy is a promising approach to the privacy-preserving
release of data: it offers a strong guaranteed bound on the increase
in harm that a user incurs as a result of participating in a
differentially private data analysis.
Researchers in differential privacy come from several area of computer
science as algorithms, programming languages, security, databases,
machine learning, as well as from several areas of statistics and data
analysis. The workshop is intended to be an occasion for researchers
from these different research areas to discuss the recent developments
in the theory and practice of differential privacy.
**Submissions**
The overall goal of TPDP is to stimulate the discussion on the
relevance of differentially private data analyses in practice. For
this reason, we seek contributions from different research areas of
computer science and statistics.
Authors are invited to submit a short abstract (4-5 pages maximum) of
their work by January 23, 2015. Abstracts must be written in English
and be submitted as a single PDF file at the EasyChair page for TPDP:
https://easychair.org/conferences/?conf=3Dtpdp2015
Submissions will be judged on originality, relevance, interest and
clarity. Submission should describe novel works or works that have
already appeared elsewhere but that can stimulate the discussion
between the different communities. Accepted abstracts will
be presented at the workshop.
The workshop will not have formal proceedings, but we plan to have a
special issue of the Journal of Privacy and Confidentiality devoted to
TPDP. Authors presenting valuable contributions at the workshop will
be invited to submit a journal version of their work right after the
workshop.
**Important Dates**
-January 23, 2015 - Abstract Submission
-February 10, 2015 - Notification
-February 14, 2015 - Deadline early registration ETAPS
-April 18, 2015 - Workshop
-May 15, 2015 Deadline for journal special issue
**Topics**
Specific topics of interest for the workshop include (but are not limited t=
o):
theory of differential privacy,
verification techniques for differential privacy,
programming languages for differential privacy,
models for differential privacy,
trade-offs between privacy protection and analytic utility,
differential privacy and surveys,
relaxations of the differential privacy definition,
differential privacy vs other privacy notions and methods,
differential privacy and accuracy,
practical differential privacy,
implementations for differential privacy,
differential privacy and security,
applications of differential privacy.
**Invited Speakers**
Jonathan Ullman - Simons Fellow at Columbia University,
Another invited speaker joint with HotSpot'15 to be confirmed.
**Program Committee**
Gilles Barthe - IMDEA Software
Konstantinos Chatzikokolakis - CNRS and LIX, Ecole Polytechnique
Kamalika Chaudhuri - UC San Diego
Graham Cormode - University of Warwick
George Danezis - University College London
Marco Gaboardi - University of Dundee
Matteo Maffei - CISPA, Saarland University
Catuscia Palamidessi - INRIA and LIX, Ecole Polytechnique
Benjamin C. Pierce - University of Pennsylvania
Aaron Roth - University of Pennsylvania
David Sands - Chalmers University of Technology
Chris Skinner - London School of Economics
Adam Smith - Pennsylvania State University
Carmela Troncoso - Gradiant
Salil Vadhan - Harvard University

Today we'll talk about infinitely repeated games. In an infinitely repeated game, n players repeatedly, in an infinite number of stages, play actions and obtain payoffs based on some commonly known stage game. Since the game is infinitely repeated, in order to make sense of players total payoff, we employ a discount factor delta that specifies how much less valuable a dollar is tomorrow compared to a dollar today. (delta is some number in [0, 1) ). In games of perfect monitoring, players perfectly observe what actions each of their opponents have played in past rounds, but in large n player games, it is much more natural to think about games of imperfect monitoring, in which agents see only some noisy signal of what their opponents have played.

For example, one natural signal players might observe in an anonymous game is a noisy histogram estimating what fraction of the population has played each type of action. (This is the kind of signal you might get if you see a random subsample of what people play -- for example, you have an estimate of how many people drove on each road on the way to work today by looking at traffic reports). Alternately, there may be some low dimensional signal (like the market price of some good) that everyone observes that is computed as a randomized function of everyone's actions today (e.g. how much of the good each person produced).

A common theme in repeated games of all sorts are folk theorems. Informally, these theorems state that in repeated games, we should expect a huge multiplicity of equilibria, well beyond the equilibria we would see in the corresponding one-shot stage game. This is because players observe each other's past behavior, and so can threaten each other to behave in prescribed ways or else face punishment. Whether or not a folk theorem is a positive result or a negative result depends on whether you want to design behavior, or predict behavior. If you are a mechanism designer, a folk theorem might be good news -- you can try and encourage equilibrium behavior that has higher welfare than any equilibrium of the stage game. However, if you want to predict behavior, it is bad news -- there are now generically a huge multiplicity of very different equilibria, and some of them have much worse welfare than any equilibrium of the stage game.

In large games, many natural signaling structures produce signal distributions that are differentially private in the actions of the players, where the privacy parameters tends to 0 as the size of the game gets large, and

In any such game, for any discount factor delta, as the size of the game gets large, the set of equilibria of the repeated game collapse to the set of equilibria of the stage game. In other words, there are no "folk theorem equilibria" -- only the equilibria that already existed in the one shot game.

This could be interpreted in a couple of ways. On the one hand, this means that in large games, it might be harder to sustain cooperation (which is a negative result). On the other hand, since it shrinks the set of equilibria, it means that adding noise to the signaling structure in a large game generically improves the price of anarchy over equilibria of the repeated game, which is a positive result.

Friday, April 04, 2014

In the 10th lecture in our privacy and mechanism design class, we consider the problem of running an ascending price auction. An ascending price auction is just a generalization of what you normally see as an "auction" on TV -- rather than submitting your valuation in some kind of one-shot protocol, the prices of the goods gradually rise, and you take turns with other bidders making bids on the goods as a function of the current prices.

Why would you want to run such an auction when the VCG mechanism already can provide welfare optimal outcomes for every social choice function, while making truthful reporting a dominant strategy? People quote a couple of reasons:

It might be hard to actually report your full valuation: in principle, you need to figure out exactly your value for every bundle you might receive, and its difficult to pin down a number. In an ascending price auction, all you need to do is be able to point to your favorite good (or bundle of goods) that you would buy if the current prices were the final prices, which is often an easier task.

An ascending price auction can end without you having to reveal your full type. For example, in a single item second price auction, the highest bidder never has to reveal (even to the auctioneer) his value for the good -- only that it is higher than that of the second highest bidder. Hence, people might prefer such auctions for "privacy" reasons.

In an ascending price auction, "truthful" reporting doesn't make sense, since nobody ever asks you to report your type. But we can ask for "sincere bidding", in which bidders truthfully bid on the item at each round that is their favorite, given the current prices. But there is a problem: we typically can't implement sincere bidding as a dominant strategy, because of the problem of threats. Consider the following simple example:

Suppose we have two unit demand bidders 1 and 2, and two goods for sale a and b. We have v_{1,a} = 1, v_{1,b} = epsilon and v_{2,a} = 1/2, v_{2, b} = 1/2 - \epsilon. Suppose moreover that bidder 2 takes the following strategy: "Bid on good a. If bidder 1 bids on good a, then outbid him on whatever he bids on until the price is > 1.'' Against this strategy, bidder 1 cannot obtain non-negative utility if he bids on his favorite good (a), and so his best response is to place an insincere bid on good 2. Moreover, bidder 2 has a clear motivation to take this threatening position -- he obtains substantially higher payoff than if players followed sincere bidding, since he gets his most preferred good without any competition. As a result of instances like these, typically ascending price auctions can implement sincere bidding at best as an (ex-post) Nash equilibirum.

In this lecture, we talk about how to implement an ascending auction such that the prices are differentially private in the bidding strategies of the players (and the allocation in the end is jointly differentially private). This fixes two of the problems above:

The privacy guaranteed by the ascending price auction is no longer hand-wavy and qualitative, but rather precise and quantitative.

We get sincere bidding as an asymptotic ex-post dominant strategy for all players.

To get this result, we need only a mild large-market assumption: that the "supply" of each good is modestly large compared to the number of different types of goods -- but crucially we need to assume nothing about how bidder preferences are generated.

The intuition, which we will appeal to again later, is that by running the auction privately, we have eliminated the possibility that players can distort incentives by threatening each other.